A Review of Wound Infusion With Local Anesthetics in Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: Suboptimal pain management is a common, yet largely unrecognized, problem in the postsurgical patient population. Current treatment protocols heavily rely on opioid use and, though generally effective in providing pain relief, are associated with multiple side effects. The present systematic review aims to offer plastic surgeons insight into the current state of literature on prolonged local anesthetic wound infusion regimens, evaluating both their efficacy in lowering pain scores and the potential opioid-sparing effect. METHODS: A comprehensive literature search of the Medline, Embase, and Cochrane Library databases was performed to identify relevant studies published between 1980 and December 2017 evaluating the use of prolonged local anesthetic wound infusion for postoperative pain management in plastic surgery. RESULTS: A total of 28 articles were selected, including 3904 patients. The overall infection rate in all patients treated with postsurgical local anesthetic wound infiltration was 0.28% (7/2536). There were no reported cases of systemic toxicity. An opioid-sparing effect was found in 92% (12/13) of studies when compared to an active comparator and 88% (7/8) of those comparing to placebo. Pain scores were decreased in 90% (9/10) of studies comparing wound infiltration to narcotic-based regimens and in 67% (6/9) of those comparing to placebo. CONCLUSIONS: Continuous or intermittent wound infusion is safe and effective in reducing pain scores and opioid consumption in plastic surgery. Though the overall pain-lowering effect appears to be modest, ease of catheter insertion and patient satisfaction make this technique an alluring alternative to more validated approaches such as neuraxial or peripheral nerve blocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".